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Corp-o-Rate-Community
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entity-references

Tasks:
Text Classification
Feature Extraction
Languages:
English
Size:
1M<n<10M
Tags:
entity-linking
named-entity-recognition
knowledge-base
organizations
people
sqlite
License:
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metadata 
language:
  - en
license: apache-2.0
task_categories:
  - text-classification
  - feature-extraction
tags:
  - entity-linking
  - named-entity-recognition
  - knowledge-base
  - organizations
  - people
  - sqlite
  - vector-search
  - embeddings
size_categories:
  - 1M<n<10M
pretty_name: Entity References Database
configs:
  - config_name: full
    description: Full database with complete source metadata
  - config_name: lite
    description: Core fields + embeddings only (recommended)

Entity References Database

A comprehensive entity database for organizations, people, roles, and locations with 768-dimensional embeddings for semantic matching. Built from authoritative sources (GLEIF, SEC, Companies House, Wikidata) for entity linking and named entity disambiguation.

Dataset Description

Dataset Summary

This dataset provides fast lookup and qualification of named entities using vector similarity search. It stores records from authoritative global sources with embeddings generated by google/embeddinggemma-300m (768 dimensions).

Key Features:

  • 8M+ organization records from GLEIF, SEC Edgar, Companies House, and Wikidata
  • Notable people including executives, politicians, athletes, artists, and more
  • Roles and locations with hierarchical relationships
  • Vector embeddings for semantic similarity search
  • Canonical linking across sources (same entity from multiple sources linked)

Supported Tasks

  • Entity Linking: Match extracted entity mentions to canonical database records
  • Named Entity Disambiguation: Distinguish between entities with similar names
  • Knowledge Base Population: Enrich extracted entities with identifiers and metadata

Languages

English (en)

Dataset Structure

Schema (v2 - Normalized)

The database uses SQLite with the sqlite-vec extension for vector similarity search.

Organizations Table

Column Type Description
id INTEGER Primary key
qid INTEGER Wikidata QID as integer (e.g., 2283 for Q2283)
name TEXT Organization name
name_normalized TEXT Lowercased, normalized name
source_id INTEGER FK Reference to source_types
source_identifier TEXT LEI, CIK, Company Number, etc.
region_id INTEGER FK Reference to locations
entity_type_id INTEGER FK Reference to organization_types
from_date TEXT Founding/registration date (ISO format)
to_date TEXT Dissolution date (ISO format)
canon_id INTEGER ID of canonical record
canon_size INTEGER Size of canonical group
record JSON Full source record (omitted in lite)

People Table

Column Type Description
id INTEGER Primary key
qid INTEGER Wikidata QID as integer
name TEXT Display name
name_normalized TEXT Lowercased, normalized name
source_id INTEGER FK Reference to source_types
source_identifier TEXT QID, Owner CIK, Person number
country_id INTEGER FK Reference to locations
person_type_id INTEGER FK Reference to people_types
known_for_role_id INTEGER FK Reference to roles
known_for_org TEXT Organization name
known_for_org_id INTEGER FK Reference to organizations
from_date TEXT Role start date (ISO format)
to_date TEXT Role end date (ISO format)
birth_date TEXT Date of birth (ISO format)
death_date TEXT Date of death (ISO format)
record JSON Full source record (omitted in lite)

Roles Table

Column Type Description
id INTEGER Primary key
qid INTEGER Wikidata QID (e.g., 484876 for CEO Q484876)
name TEXT Role name (e.g., "Chief Executive Officer")
name_normalized TEXT Normalized name
source_id INTEGER FK Reference to source_types
canon_id INTEGER ID of canonical role

Locations Table

Column Type Description
id INTEGER Primary key
qid INTEGER Wikidata QID (e.g., 30 for USA Q30)
name TEXT Location name
name_normalized TEXT Normalized name
source_id INTEGER FK Reference to source_types
source_identifier TEXT ISO code (e.g., "US", "CA")
parent_ids TEXT JSON Parent location IDs in hierarchy
location_type_id INTEGER FK Reference to location_types

Embedding Tables (sqlite-vec)

Table Columns
organization_embeddings org_id INTEGER, embedding FLOAT[768]
organization_embeddings_scalar org_id INTEGER, embedding INT8[768]
person_embeddings person_id INTEGER, embedding FLOAT[768]
person_embeddings_scalar person_id INTEGER, embedding INT8[768]

Scalar (int8) embeddings provide 75% storage reduction with ~92% recall at top-100.

Enum Lookup Tables

Table Values
source_types gleif, sec_edgar, companies_house, wikidata
people_types executive, politician, government, military, legal, professional, academic, artist, media, athlete, entrepreneur, journalist, activist, scientist, unknown
organization_types business, fund, branch, nonprofit, ngo, foundation, government, international_org, political_party, trade_union, educational, research, healthcare, media, sports, religious, unknown
simplified_location_types continent, country, subdivision, city, district, other

Data Splits

Config Size Contents
entities-lite.db ~50GB Core fields + embeddings only
entities.db ~74GB Full records with source metadata

The lite version is recommended for most use cases.

Dataset Creation

Source Data

Organizations

Source Records Identifier Coverage
GLEIF ~3.2M LEI (Legal Entity Identifier) Global companies with LEI
SEC Edgar ~100K+ CIK (Central Index Key) All SEC filers
Companies House ~5M Company Number UK registered companies
Wikidata Variable QID Notable companies worldwide

People

Source Records Identifier Coverage
Wikidata Variable QID Notable people with English Wikipedia
SEC Form 4 ~280K/year Owner CIK US public company insiders
Companies House ~15M+ Person number UK company officers

Embedding Model

Property Value
Model google/embeddinggemma-300m
Dimensions 768
Framework sentence-transformers
Size ~300M parameters

Canonicalization

Records are linked across sources based on:

Organizations:

  1. Same LEI (globally unique)
  2. Same ticker symbol
  3. Same CIK
  4. Same normalized name + region

People:

  1. Same Wikidata QID
  2. Same normalized name + same organization
  3. Same normalized name + overlapping date ranges

Source priority: gleif > sec_edgar > companies_house > wikidata

Usage

Installation 

pip install corp-extractor

Download 

# Download lite version (recommended)
corp-extractor db download

# Download full version
corp-extractor db download --full

Storage location: ~/.cache/corp-extractor/entities-v2.db

Search 

# Search organizations
corp-extractor db search "Microsoft"

# Search people
corp-extractor db search-people "Tim Cook"

# Search roles
corp-extractor db search-roles "CEO"

# Search locations
corp-extractor db search-locations "California"

# Check database status
corp-extractor db status

Python API 

from statement_extractor.database import OrganizationDatabase, PersonDatabase

# Search organizations
org_db = OrganizationDatabase()
matches = org_db.search_by_name("Microsoft Corporation", top_k=5)
for match in matches:
    print(f"{match.company.name} ({match.company.source}:{match.company.source_id})")
    print(f"  Similarity: {match.similarity_score:.3f}")

# Search people
person_db = PersonDatabase()
matches = person_db.search_by_name("Tim Cook", top_k=5)
for match in matches:
    print(f"{match.person.name} - {match.person.known_for_role} at {match.person.known_for_org}")

Use in Pipeline 

from statement_extractor.pipeline import ExtractionPipeline

pipeline = ExtractionPipeline()
ctx = pipeline.process("Microsoft CEO Satya Nadella announced new AI features.")

for stmt in ctx.labeled_statements:
    print(f"{stmt.subject_fqn} --[{stmt.statement.predicate}]--> {stmt.object_fqn}")

Technical Details

Vector Search Performance

Database Size Search Time Memory
100K records ~50ms ~500MB
1M records ~200ms ~3GB
8M records ~500ms ~20GB

Similarity Thresholds

Score Interpretation
> 0.85 Strong match (likely same entity)
0.70 - 0.85 Good match (probable same entity)
0.55 - 0.70 Moderate match (may need verification)
< 0.55 Weak match (likely different entity)

Canonical ID Format

Source Prefix Example
GLEIF LEI LEI:INR2EJN1ERAN0W5ZP974
SEC Edgar SEC-CIK SEC-CIK:0000789019
Companies House UK-CH UK-CH:00445790
Wikidata WIKIDATA WIKIDATA:Q2283

Building from Source 

# Import data sources
corp-extractor db import-gleif --download
corp-extractor db import-sec --download
corp-extractor db import-companies-house --download
corp-extractor db import-wikidata --limit 100000
corp-extractor db import-people --all --limit 50000

# Link equivalent records
corp-extractor db canonicalize

# Generate scalar embeddings (75% smaller)
corp-extractor db backfill-scalar

# Create lite version for deployment
corp-extractor db create-lite ~/.cache/corp-extractor/entities.db

Wikidata Dump Import (Recommended for Large Imports) 

# Download and import from Wikidata dump (~100GB)
corp-extractor db import-wikidata-dump --download --limit 50000

# Import only people
corp-extractor db import-wikidata-dump --download --people --no-orgs

# Import only locations
corp-extractor db import-wikidata-dump --dump dump.json.bz2 --locations --no-people --no-orgs

# Resume interrupted import
corp-extractor db import-wikidata-dump --dump dump.bz2 --resume

Considerations for Using the Data

Social Impact

This dataset enables entity linking for NLP applications. Users should be aware that:

  • Organization and people records may be incomplete or outdated
  • Historic people (deceased) are included with death_date field
  • Not all notable entities are covered

Biases

  • Coverage is weighted toward English-speaking countries (US, UK) due to source availability
  • Wikidata coverage depends on Wikipedia notability criteria
  • SEC and Companies House data is limited to their respective jurisdictions

Limitations

  • The database does not automatically deduplicate across sources
  • Embedding similarity is not perfect for entity disambiguation
  • Updates require re-importing from source data

License

Apache 2.0

Citation

If you use this dataset, please cite:

@dataset{entity_references_2024,
  title = {Entity References Database},
  author = {Corp-o-Rate-Community},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Corp-o-Rate-Community/entity-references}
}

Dataset Card Authors

Corp-o-Rate-Community

Dataset Card Contact

Open an issue on the GitHub repository for questions or feedback.